# Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests

arXiv: https://arxiv.org/abs/2306.05715 · HTML: https://mlsanity.com/paper/2306.05715/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 96 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book](https://mlsanity.com/paper/2211.02265/) (2022)
- [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://mlsanity.com/paper/2210.15157/) (2022)
- [Using Large Language Models to Enhance Programming Error Messages](https://mlsanity.com/paper/2210.11630/) (2022)
- ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023)
- [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023)
- [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023)
- [Automatically Generating CS Learning Materials with Large Language Models](https://mlsanity.com/paper/2212.05113/) (2022)
- [Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing](https://mlsanity.com/paper/2211.04715/) (2022)

## Built on by (most cited first, 30 of 46)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
- [Rubric Is All You Need: Enhancing LLM-based Code Evaluation With Question-Specific Rubrics](https://mlsanity.com/paper/2503.23989/) (2025)
- [Exploring the Potential of Large Language Models to Generate Formative Programming Feedback](https://mlsanity.com/paper/2309.00029/) (2023)
- [CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2309.01940/) (2023)
- [Evaluating Contextually Personalized Programming Exercises Created with Generative AI](https://mlsanity.com/paper/2407.11994/) (2024)
- [Evaluating the Application of Large Language Models to Generate Feedback in Programming Education](https://mlsanity.com/paper/2403.09744/) (2024)
- [How Do Programming Students Use Generative AI?](https://mlsanity.com/paper/2501.10091/) (2025)
- [Iris: An AI-Driven Virtual Tutor For Computer Science Education](https://mlsanity.com/paper/2405.08008/) (2024)
- [Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation](https://mlsanity.com/paper/2407.05437/) (2024)
- [AI-enhanced Auto-correction of Programming Exercises: How Effective is GPT-3.5?](https://mlsanity.com/paper/2311.10737/) (2023)
- [Evaluating Language Models for Generating and Judging Programming Feedback](https://mlsanity.com/paper/2407.04873/) (2024)
- [Learning from Teaching Assistants to Program with Subgoals: Exploring the Potential for AI Teaching Assistants](https://mlsanity.com/paper/2309.10419/) (2023)
- [A Survey on Human-AI Collaboration with Large Foundation Models](https://mlsanity.com/paper/2403.04931/) (2024)
- [Can ChatGPT Play the Role of a Teaching Assistant in an Introductory Programming Course?](https://mlsanity.com/paper/2312.07343/) (2023)
- ["ChatGPT Is Here to Help, Not to Replace Anybody" -- An Evaluation of Students' Opinions On Integrating ChatGPT In CS Courses](https://mlsanity.com/paper/2404.17443/) (2024)
- [Leveraging Lecture Content for Improved Feedback: Explorations with GPT-4 and Retrieval Augmented Generation](https://mlsanity.com/paper/2405.06681/) (2024)
- ["Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models](https://mlsanity.com/paper/2403.09409/) (2024)
- [CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education](https://mlsanity.com/paper/2407.10246/) (2024)
- [Benchmarking Large Language Models on Homework Assessment in Circuit Analysis](https://mlsanity.com/paper/2506.06390/) (2025)
- [The Responsible Development of Automated Student Feedback with Generative AI](https://mlsanity.com/paper/2308.15334/) (2023)
- [Enhancing Programming Error Messages in Real Time with Generative AI](https://mlsanity.com/paper/2402.08072/) (2024)
- [Exploring Student Behaviors and Motivations using AI TAs with Optional Guardrails](https://mlsanity.com/paper/2504.11146/) (2025)
- [Benchmarking Educational Program Repair](https://mlsanity.com/paper/2405.05347/) (2024)
- ["Give me the code" -- Log Analysis of First-Year CS Students' Interactions With GPT](https://mlsanity.com/paper/2411.17855/) (2024)
- [Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners](https://mlsanity.com/paper/2412.12800/) (2024)

## Abstract (arXiv)

Background and Context: Over the past year, large language models (LLMs) have taken the world by storm. In computing education, like in other walks of life, many opportunities and threats have emerged as a consequence. Objectives: In this article, we explore such opportunities and threats in a specific area: responding to student programmers' help requests. More specifically, we assess how good LLMs are at identifying issues in problematic code that students request help on. Method: We collected a sample of help requests and code from an online programming course. We then prompted two different LLMs (OpenAI Codex and GPT-3.5) to identify and explain the issues in the students' code and assessed the LLM-generated answers both quantitatively and qualitatively. Findings: GPT-3.5 outperforms Codex in most respects. Both LLMs frequently find at least one actual issue in each student program (GPT-3.5 in 90% of the cases). Neither LLM excels at finding all the issues (GPT-3.5 finding them 57% of the time). False positives are common (40% chance for GPT-3.5). The advice that the LLMs provide on the issues is often sensible. The LLMs perform better on issues involving program logic rather than on output formatting. Model solutions are frequently provided even when the LLM is prompted not to. LLM responses to prompts in a non-English language are only slightly worse than responses to English prompts. Implications: Our results continue to highlight the utility of LLMs in programming education. At the same time, the results highlight the unreliability of LLMs: LLMs make some of the same mistakes that students do, perhaps especially when formatting output as required by automated assessment systems. Our study informs teachers interested in using LLMs as well as future efforts to customize LLMs for the needs of programming education.